Deep Learning-based Segmentation of CT Scans Predicts Disease Progression and Mortality in IPF.
Muhunthan ThillaiJustin M OldhamAlessandro RuggieroFahdi KanavatiTom McLellanGauri SainiSimon R JohnsonFrancois-Xavier BleAdnan AzimKristoffer OstridgeAdam PlattMaria BelvisiToby M MaherPhilip L MolyneauxPublished in: American journal of respiratory and critical care medicine (2024)
Automated models can rapidly segment IPF CT scans, providing prognostic near and long-term information, which could be used in routine clinical practice or as key trial endpoints. This article is open access and distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).
Keyphrases
- deep learning
- dual energy
- computed tomography
- clinical practice
- contrast enhanced
- idiopathic pulmonary fibrosis
- image quality
- convolutional neural network
- artificial intelligence
- magnetic resonance imaging
- positron emission tomography
- machine learning
- study protocol
- magnetic resonance
- minimally invasive
- clinical trial
- phase ii
- risk factors
- phase iii
- healthcare
- health information
- randomized controlled trial
- high throughput